The latter methods (e.g., Agricultural DRASTIC) were developed with a particular type of contamination in mind, but generally lack any real specificity among the contaminants considered. For example, a method that lumps all agricultural contaminants clearly lacks specificity, given the wide range of properties among pesticides and other agricultural chemicals.

Process-Based Simulation Models

Everything must be made as simple as possible, but not simpler.

—Albert Einstein

Process-based simulation models are distinguished from all other methods because many of them attempt to predict contaminant transport in both space and time. For example, simulations of one-dimensional transport in the unsaturated soil zone may predict contaminant concentrations with depth at discrete time intervals during and after the time the contaminant is applied to the land surface. Similarly, the computer algorithms available for contaminant transport in the saturated and unsaturated zones (NRC 1990) predict the vertical and areal extent of contamination with time and mathematically incorporate many of the physical, chemical, and microbial processes in the unsaturated and saturated zones.

Process-based models can be used in both regional and site-specific studies and have been developed and applied primarily by research scientists rather than by regulators. The complex simulation models for solving coupled and/or multiphase contaminant transport in two or three dimensions have been used almost exclusively to evaluate physical, biological, and chemical controls in hypothetical settings or well-evaluated local incidences of contamination (NRC 1990). Such complex models have not been used to evaluate ground water vulnerability on a regional scale; therefore, this discussion will focus on simpler process-based models of one-dimensional transport through the vadose zone.

Table 3.5 indicates the various process representations used in several simulation models that have been used to predict pesticide behavior in the unsaturated zone. Outputs from three of these models are detailed in Table 3.6. These tables are included for illustrative purposes; more recent versions of these models include enhancements in areas of process representation, input parameter estimation, and output capabilities. The models listed in these tables differ in complexity. LEACHM is the most complex in terms of the number of processes included and the most sophisticated in terms of process description. Models such as LEACHM have large data requirements, but they offer the flexibility of being applicable to more diverse scenarios and provide detailed outputs (see Table 3.6). Models such as GLEAMS and PRZM are designed to assist in management decisions;

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